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Analysis of gene copy number changes in tumor phylogenetics

Overview of attention for article published in Algorithms for Molecular Biology, September 2016
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Title
Analysis of gene copy number changes in tumor phylogenetics
Published in
Algorithms for Molecular Biology, September 2016
DOI 10.1186/s13015-016-0088-2
Pubmed ID
Authors

Jun Zhou, Yu Lin, Vaibhav Rajan, William Hoskins, Bing Feng, Jijun Tang

Abstract

Evolution of cancer cells is characterized by large scale and rapid changes in the chromosomal  landscape. The fluorescence in situ hybridization (FISH) technique provides a way to measure the copy numbers of preselected genes in a group of cells and has been found to be a reliable source of data to model the evolution of tumor cells. Chowdhury et al. (Bioinformatics 29(13):189-98, 23; PLoS Comput Biol 10(7):1003740, 24) recently develop a computational model for tumor progression driven by gains and losses in cell count patterns obtained by FISH probes. Their model aims to find the rectilinear Steiner minimum tree (RSMT) (Chowdhury et al. in Bioinformatics 29(13):189-98, 23) and the duplication Steiner minimum tree (DSMT) (Chowdhury et al. in PLoS Comput Biol 10(7):1003740, 24) that describe the progression of FISH cell count patterns over its branches in a parsimonious manner. Both the RSMT and DSMT problems are NP-hard and heuristics are required to solve the problems efficiently. In this paper we propose two approaches to solve the RSMT problem, one inspired by iterative methods to address the "small phylogeny" problem (Sankoff et al. in J Mol Evol 7(2):133-49, 27; Blanchette et al. in Genome Inform 8:25-34, 28), and the other based on maximum parsimony phylogeny inference. We further show how to extend these heuristics to obtain solutions to the DSMT problem, that models large scale duplication events. Experimental results from both simulated and real tumor data show that our methods outperform previous heuristics (Chowdhury et al. in Bioinformatics 29(13):189-98, 23; Chowdhury et al. in PLoS Comput Biol 10(7):1003740, 24) in obtaining solutions to both RSMT and DSMT problems. The methods introduced here are able to provide more parsimony phylogenies compared to earlier ones which are consider better choices.

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Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 50%
Researcher 2 33%
Student > Ph. D. Student 1 17%
Readers by discipline Count As %
Computer Science 4 67%
Environmental Science 1 17%
Agricultural and Biological Sciences 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2016.
All research outputs
#18,472,072
of 22,889,074 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#243,786
of 321,009 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
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